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Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.

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Title: Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.
Authors: Tang, Tianpei1,2 (AUTHOR), Yuan, Meining1 (AUTHOR), Zhang, Nan3 (AUTHOR), Wang, Hua1,4 (AUTHOR) hwang191901@gmail.com, Guo, Yuntao5 (AUTHOR), Shi, Quan1 (AUTHOR)
Source: Transportation Research: Part F. Feb2025, Vol. 109, p1049-1062. 14p.
Subjects: Artificial neural networks, Planned behavior theory, Traffic violations, Econometric models, Road safety measures
Abstract: • An innovative six-step analytical framework integrating ANNs was developed. • Determinants of various red-light running (RLR) groups were studied among e-bikers. • A network weight-based approach quantified the impacts of influencing factors. • Our model surpasses existing models in understanding e-bikers' RLR behavior. Understanding red-light running (RLR) behavior among electric bikers (e-bikers) is critical for addressing the high accident rates associated with this behavior. Traditional analytical methods, such as econometric modeling, often fail to capture the non-linear dynamics of traffic violations, limiting their effectiveness in exploring the complexity of such behaviors. Conversely, Artificial Neural Networks (ANNs) excel in handling non-linear relationships but lack interpretability, making their application in decision-making challenging. This study introduces an innovative six-step analytical framework that integrates hybrid ANNs with the Theory of Planned Behavior (TPB). This integration utilizes a network weight-based approach to quantify the impacts of influencing factors within the ANNs. The results demonstrate that this hybrid framework not only enhances predictive accuracy but also provides a deeper understanding of the motivational drivers behind e-bikers' RLR behavior. The study identifies significant behavioral heterogeneities across e-biker groups, emphasizing the need for targeted interventions. Based on these findings, a multi-faceted intervention strategy is proposed, combining educational campaigns, regulatory measures, and community engagement efforts tailored to distinct behavioral profiles. This research provides a robust foundation for developing safety improvement programs that aim to reduce e-biker accidents and improve overall road safety. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• An innovative six-step analytical framework integrating ANNs was developed. • Determinants of various red-light running (RLR) groups were studied among e-bikers. • A network weight-based approach quantified the impacts of influencing factors. • Our model surpasses existing models in understanding e-bikers' RLR behavior. Understanding red-light running (RLR) behavior among electric bikers (e-bikers) is critical for addressing the high accident rates associated with this behavior. Traditional analytical methods, such as econometric modeling, often fail to capture the non-linear dynamics of traffic violations, limiting their effectiveness in exploring the complexity of such behaviors. Conversely, Artificial Neural Networks (ANNs) excel in handling non-linear relationships but lack interpretability, making their application in decision-making challenging. This study introduces an innovative six-step analytical framework that integrates hybrid ANNs with the Theory of Planned Behavior (TPB). This integration utilizes a network weight-based approach to quantify the impacts of influencing factors within the ANNs. The results demonstrate that this hybrid framework not only enhances predictive accuracy but also provides a deeper understanding of the motivational drivers behind e-bikers' RLR behavior. The study identifies significant behavioral heterogeneities across e-biker groups, emphasizing the need for targeted interventions. Based on these findings, a multi-faceted intervention strategy is proposed, combining educational campaigns, regulatory measures, and community engagement efforts tailored to distinct behavioral profiles. This research provides a robust foundation for developing safety improvement programs that aim to reduce e-biker accidents and improve overall road safety. [ABSTRACT FROM AUTHOR]
ISSN:13698478
DOI:10.1016/j.trf.2025.01.027